
The encoding module needs to encode not only \(P\)  and \(A_i\)  but also the conversational history.

General observation about information-seeking dialog in humans suggests that the starting dialog-turns tend to focus on the beginning chunks of the passage and shift focus to the later chunks as the conversation progresses [1]}. The model is thus expected to capture these focal shifts during a conversation and reason pragmatically, instead of only matching lexically or via paraphrasing.

Multi-turn conversations are generally incremental and co-referential. These conversational dialogs are either drilling down (the current question is a request for more information about the topic), shifting topic (the current question is not immediately relevant to something previously discussed), returning topic (the current question is asking about a topic again after it had previously been shifted away from), clarification of topic, or definition of an entity [2]}. The model should, therefore, be able to take context from history which may or may not be immediate.

